The Analysis Of Manufacturing System Performance Evaluation Based On Improved Slacks Based Measure Model

Manufacturing system is a manufacturing process that has a lot of inputs and outputs.The whole process involves lots of indicator variables, so it is important to set up areasonable indicator variables system of performance measurement for manufacturingsystem for not only increasing productivity, reduce cost but also improve the quality of theproduct. Manufacturing system performance measure is a process that putting a number ofresources and generate a certain number of output under certain conditions. The basiccharacteristic is having a certain input and output, to use smaller input for a larger outputthrough a variety of measures in the conversion of input and output. Thereforemanufacturing system performance evaluation is essentially a comprehensive evaluationproblem with multiple inputs/outputs. An effective way to solve this problem is to use dataenvelopment analysis measure.This article researches the present situation of the performance evaluation ofmanufacturing system and the development of DEA model and summarized. Thenintroduces the basic principle and basic model of DEA, and introduced Slacks-basedmeasure, the main model of this paper. The model put the slack variable directly into theobjective function to solve the relaxation problem of inputs and outputs, it will evaluate themanufacturing performance more effectively. In order to make manufacturing systemperformance evaluation more in line with the actual production, this article constructedthree kinds of improved model. Introducing the concept of entropy and combining with thesuper efficiency SBM（Slacks-Based Measure,SBM） to construct a SBM model withentropy constraint (entropy-super efficiency SBM)for researching the relative importanceof indicators; Introduced the concept of triangular fuzzy number α-cut,constructed a fuzzysuper-SBM to solve the uncertainty of indicators in practical problems; Consideringinternal network structure of manufacturing systems, open the manufacturing system blackbox and constructed a network-SBM to study the internal structure which considering theunexpected outputs. For each model, there are corresponding performance evaluation caseof manufacturing systems, comparing with the results by the new model and the traditionalmodel to prove that the new models have theoretical value and practical value, and toprovide the benchmark and improvement direction of manufacturing performance which isof great significance for manufacturing management.